Automated segmentation of pulmonary lobes in chest CT scans using evolving surfaces

Automated segmentation of the pulmonary lobes from chest CT scans is a challenging problem that is yet to be solved reliably. Therefore there is a need for a semi-automated solution in the case where the automated solution fails. We present an approach that can be used for correcting an existing lobe segmentation or segmenting the lobes from scratch in a semi-automatic manner. The method is based on an iterative approach that evolves a surface based on a voxel based fissure confidence function, smooth prior and user input points. An advantage of the proposed method is that it takes into account both inputs from user and the appearance of fissures in the image, which in turn reduces the number of user interactions required. The proposed method was trained and tuned on 18 CT scans, and tested on 22 CT scans from different subjects with either idiopathic pulmonary fibrosis or severe emphysema. On average, the proposed method requires 37 user drawn line segments, which are mostly short, to segment all lobes accurately. We did not notice a large difference in the number of required line segments between starting from scratch or correcting lobe segmented from an automated method, as it usually requires only two lines in two different view plane from the user to obtain a relatively accurate fissure from scratch.